稳态Fokker-Planck学习收敛性的特征
Characterization of the convergence of stationary Fokker-Planck learning
Universidad Autónoma de Nuevo León（新莱昂自治大学）
摘要：
研究了估计随机搜索过程渐近密度的平稳Fokker-Planck算法的收敛性。理论和经验参数的估计的收敛性的表征的情况下，可分离和不可分离的非线性优化问题。本文概述了固定Fokker-Planck学习的收敛性对人工神经网络模型参数推断的影响。
英文摘要：
The convergence properties of the stationary Fokker-Planck algorithm for the estimation of the asymptotic density of stochastic search processes is studied. Theoretical and empirical arguments for the characterization of convergence of the estimation in the case of separable and nonseparable nonlinear optimization problems are given. Some implications of the convergence of stationary Fokker-Planck learning for the inference of parameters in artificial neural network models are outlined.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
